Recently, the use of FL has been increasing in the medical field. In [1]}, Huang et al. proposed Loss-based Adaptive Boosting Federated Averaging (LoAdaBoost FedAvg) on critical care database data called as MIMIC-III [2]}. In this method, the collaborators with higher losses than the previous round median loss are retrained before sending to the server for model aggregation. In [3]}, Li et al. have proposed a federated learning system for brain tumor segmentation on BraTS 2018 dataset [4]} and have shown the trade-off between privacy protection costs and model performance. Similarly, in [5]}, Sheller et al. have compared federated learning and other data private collaborative learning approaches such as institutional incremental learning and cyclic institutional incremental learning on brain tumor segmentation task. This study has shown that FL can overcome institutional biases and form a global model that has better generalization where data amount and data diversity are inadequate. In [6]}, Dou et al. have used FL architecture to detect chest CT abnormalities in COVID-19 patients and showed that federated global model outperforms in terms of generalizability on external datasets better than individual models and their ensemble.
